Overview

Dataset statistics

Number of variables18
Number of observations893
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.7 KiB
Average record size in memory144.1 B

Variable types

Numeric7
Categorical8
Text3

Alerts

CPU is highly overall correlated with RamHigh correlation
Ram is highly overall correlated with CPU and 1 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with Unnamed: 0.1High correlation
Unnamed: 0.1 is highly overall correlated with Unnamed: 0High correlation
price is highly overall correlated with Ram and 2 other fieldsHigh correlation
resolution_height is highly overall correlated with price and 1 other fieldsHigh correlation
resolution_width is highly overall correlated with price and 1 other fieldsHigh correlation
ROM is highly imbalanced (55.5%)Imbalance
ROM_type is highly imbalanced (83.9%)Imbalance
OS is highly imbalanced (75.7%)Imbalance
warranty is highly imbalanced (75.6%)Imbalance
Unnamed: 0.1 is uniformly distributedUniform
Unnamed: 0.1 has unique valuesUnique
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2023-12-25 21:18:52.641999
Analysis finished2023-12-25 21:19:05.777533
Duration13.14 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct893
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.1355
Minimum0
Maximum930
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:05.982929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45.6
Q1235
median467
Q3702
95-th percentile885.4
Maximum930
Range930
Interquartile range (IQR)467

Descriptive statistics

Standard deviation270.20977
Coefficient of variation (CV)0.57843981
Kurtosis-1.1999823
Mean467.1355
Median Absolute Deviation (MAD)234
Skewness-0.0090534777
Sum417152
Variance73013.319
MonotonicityStrictly increasing
2023-12-26T03:19:06.205035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
627 1
 
0.1%
616 1
 
0.1%
617 1
 
0.1%
618 1
 
0.1%
619 1
 
0.1%
620 1
 
0.1%
621 1
 
0.1%
622 1
 
0.1%
623 1
 
0.1%
Other values (883) 883
98.9%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
930 1
0.1%
929 1
0.1%
928 1
0.1%
927 1
0.1%
926 1
0.1%
925 1
0.1%
924 1
0.1%
923 1
0.1%
922 1
0.1%
921 1
0.1%

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct893
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.38298
Minimum0
Maximum1019
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:06.677351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.6
Q1265
median531
Q3784
95-th percentile973.4
Maximum1019
Range1019
Interquartile range (IQR)519

Descriptive statistics

Standard deviation299.9166
Coefficient of variation (CV)0.57523283
Kurtosis-1.2250711
Mean521.38298
Median Absolute Deviation (MAD)260
Skewness-0.064904162
Sum465595
Variance89949.97
MonotonicityStrictly increasing
2023-12-26T03:19:06.929440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
708 1
 
0.1%
697 1
 
0.1%
698 1
 
0.1%
699 1
 
0.1%
700 1
 
0.1%
701 1
 
0.1%
702 1
 
0.1%
703 1
 
0.1%
704 1
 
0.1%
Other values (883) 883
98.9%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1019 1
0.1%
1018 1
0.1%
1017 1
0.1%
1016 1
0.1%
1015 1
0.1%
1014 1
0.1%
1013 1
0.1%
1012 1
0.1%
1011 1
0.1%
1010 1
0.1%

brand
Categorical

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
HP
186 
Lenovo
169 
Asus
157 
Dell
107 
Acer
84 
Other values (25)
190 

Length

Max length9
Median length8
Mean length4.1881299
Min length2

Characters and Unicode

Total characters3740
Distinct characters44
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.8%

Sample

1st rowHP
2nd rowHP
3rd rowAcer
4th rowLenovo
5th rowApple

Common Values

ValueCountFrequency (%)
HP 186
20.8%
Lenovo 169
18.9%
Asus 157
17.6%
Dell 107
12.0%
Acer 84
9.4%
MSI 65
 
7.3%
Samsung 28
 
3.1%
Apple 16
 
1.8%
Infinix 15
 
1.7%
LG 9
 
1.0%
Other values (20) 57
 
6.4%

Length

2023-12-26T03:19:07.136347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hp 186
20.8%
lenovo 169
18.9%
asus 157
17.6%
dell 107
12.0%
acer 84
9.4%
msi 65
 
7.3%
samsung 28
 
3.1%
apple 16
 
1.8%
infinix 15
 
1.7%
lg 9
 
1.0%
Other values (20) 57
 
6.4%

Most occurring characters

ValueCountFrequency (%)
e 403
 
10.8%
o 364
 
9.7%
s 361
 
9.7%
A 260
 
7.0%
n 240
 
6.4%
l 240
 
6.4%
u 206
 
5.5%
H 190
 
5.1%
P 187
 
5.0%
L 180
 
4.8%
Other values (34) 1109
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2518
67.3%
Uppercase Letter 1222
32.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 403
16.0%
o 364
14.5%
s 361
14.3%
n 240
9.5%
l 240
9.5%
u 206
8.2%
v 170
6.8%
r 96
 
3.8%
c 93
 
3.7%
i 83
 
3.3%
Other values (14) 262
10.4%
Uppercase Letter
ValueCountFrequency (%)
A 260
21.3%
H 190
15.5%
P 187
15.3%
L 180
14.7%
D 107
8.8%
S 93
 
7.6%
I 80
 
6.5%
M 67
 
5.5%
G 17
 
1.4%
X 10
 
0.8%
Other values (10) 31
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 3740
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 403
 
10.8%
o 364
 
9.7%
s 361
 
9.7%
A 260
 
7.0%
n 240
 
6.4%
l 240
 
6.4%
u 206
 
5.5%
H 190
 
5.1%
P 187
 
5.0%
L 180
 
4.8%
Other values (34) 1109
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 403
 
10.8%
o 364
 
9.7%
s 361
 
9.7%
A 260
 
7.0%
n 240
 
6.4%
l 240
 
6.4%
u 206
 
5.5%
H 190
 
5.1%
P 187
 
5.0%
L 180
 
4.8%
Other values (34) 1109
29.7%

name
Text

Distinct815
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:07.570262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length44
Mean length31.534155
Min length13

Characters and Unicode

Total characters28160
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique744 ?
Unique (%)83.3%

Sample

1st rowVictus 15-fb0157AX Gaming Laptop
2nd row15s-fq5007TU Laptop
3rd rowOne 14 Z8-415 Laptop
4th rowYoga Slim 6 14IAP8 82WU0095IN Laptop
5th rowMacBook Air 2020 MGND3HN Laptop
ValueCountFrequency (%)
laptop 882
 
21.8%
gaming 254
 
6.3%
2023 124
 
3.1%
15 106
 
2.6%
vivobook 105
 
2.6%
ideapad 77
 
1.9%
3 76
 
1.9%
inspiron 59
 
1.5%
pro 57
 
1.4%
14 55
 
1.4%
Other values (975) 2248
55.6%
2023-12-26T03:19:08.246459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3162
 
11.2%
p 1900
 
6.7%
o 1846
 
6.6%
a 1561
 
5.5%
1 1246
 
4.4%
0 1236
 
4.4%
t 1079
 
3.8%
L 1034
 
3.7%
5 967
 
3.4%
2 873
 
3.1%
Other values (56) 13256
47.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11046
39.2%
Uppercase Letter 6733
23.9%
Decimal Number 6580
23.4%
Space Separator 3162
 
11.2%
Dash Punctuation 511
 
1.8%
Other Punctuation 98
 
0.3%
Format 29
 
0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 1034
15.4%
G 524
 
7.8%
I 484
 
7.2%
N 458
 
6.8%
A 392
 
5.8%
S 365
 
5.4%
P 298
 
4.4%
X 288
 
4.3%
V 276
 
4.1%
T 241
 
3.6%
Other values (16) 2373
35.2%
Lowercase Letter
ValueCountFrequency (%)
p 1900
17.2%
o 1846
16.7%
a 1561
14.1%
t 1079
9.8%
i 813
7.4%
n 601
 
5.4%
e 397
 
3.6%
r 354
 
3.2%
m 348
 
3.2%
g 304
 
2.8%
Other values (14) 1843
16.7%
Decimal Number
ValueCountFrequency (%)
1 1246
18.9%
0 1236
18.8%
5 967
14.7%
2 873
13.3%
3 701
10.7%
4 489
 
7.4%
6 373
 
5.7%
7 276
 
4.2%
8 270
 
4.1%
9 149
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 95
96.9%
/ 3
 
3.1%
Space Separator
ValueCountFrequency (%)
3162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 511
100.0%
Format
ValueCountFrequency (%)
‎ 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17779
63.1%
Common 10381
36.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 1900
 
10.7%
o 1846
 
10.4%
a 1561
 
8.8%
t 1079
 
6.1%
L 1034
 
5.8%
i 813
 
4.6%
n 601
 
3.4%
G 524
 
2.9%
I 484
 
2.7%
N 458
 
2.6%
Other values (40) 7479
42.1%
Common
ValueCountFrequency (%)
3162
30.5%
1 1246
 
12.0%
0 1236
 
11.9%
5 967
 
9.3%
2 873
 
8.4%
3 701
 
6.8%
- 511
 
4.9%
4 489
 
4.7%
6 373
 
3.6%
7 276
 
2.7%
Other values (6) 547
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28131
99.9%
Punctuation 29
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3162
 
11.2%
p 1900
 
6.8%
o 1846
 
6.6%
a 1561
 
5.5%
1 1246
 
4.4%
0 1236
 
4.4%
t 1079
 
3.8%
L 1034
 
3.7%
5 967
 
3.4%
2 873
 
3.1%
Other values (55) 13227
47.0%
Punctuation
ValueCountFrequency (%)
‎ 29
100.0%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct464
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79907.41
Minimum9999
Maximum450039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:08.508324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9999
5-th percentile26995.4
Q144500
median61990
Q390990
95-th percentile199990
Maximum450039
Range440040
Interquartile range (IQR)46490

Descriptive statistics

Standard deviation60880.044
Coefficient of variation (CV)0.76188233
Kurtosis9.5323276
Mean79907.41
Median Absolute Deviation (MAD)22390
Skewness2.7096943
Sum71357317
Variance3.7063797 × 109
MonotonicityNot monotonic
2023-12-26T03:19:08.790661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49990 16
 
1.8%
37990 13
 
1.5%
59990 12
 
1.3%
47990 11
 
1.2%
64990 11
 
1.2%
54990 11
 
1.2%
79990 11
 
1.2%
33990 11
 
1.2%
57990 11
 
1.2%
62990 11
 
1.2%
Other values (454) 775
86.8%
ValueCountFrequency (%)
9999 1
 
0.1%
10990 3
0.3%
12990 1
 
0.1%
13990 1
 
0.1%
14490 1
 
0.1%
14990 1
 
0.1%
15990 2
0.2%
16990 1
 
0.1%
17990 1
 
0.1%
18990 2
0.2%
ValueCountFrequency (%)
450039 1
0.1%
429990 1
0.1%
420000 1
0.1%
419990 1
0.1%
415000 1
0.1%
399999 1
0.1%
390914 1
0.1%
362999 1
0.1%
344990 1
0.1%
339990 1
0.1%

spec_rating
Real number (ℝ)

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.379026
Minimum60
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:09.026444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile60
Q166
median69.323529
Q371
95-th percentile80
Maximum89
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5415547
Coefficient of variation (CV)0.07987363
Kurtosis1.3926635
Mean69.379026
Median Absolute Deviation (MAD)2.3235294
Skewness0.8621133
Sum61955.471
Variance30.708828
MonotonicityNot monotonic
2023-12-26T03:19:09.258189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
69.32352941 292
32.7%
60 48
 
5.4%
71 44
 
4.9%
70 43
 
4.8%
62 42
 
4.7%
64 39
 
4.4%
67 39
 
4.4%
66 37
 
4.1%
65 37
 
4.1%
69 35
 
3.9%
Other values (20) 237
26.5%
ValueCountFrequency (%)
60 48
5.4%
61 7
 
0.8%
62 42
4.7%
63 32
3.6%
64 39
4.4%
65 37
4.1%
66 37
4.1%
67 39
4.4%
68 6
 
0.7%
69 35
3.9%
ValueCountFrequency (%)
89 5
0.6%
88 4
 
0.4%
86 4
 
0.4%
85 6
0.7%
84 4
 
0.4%
83 9
1.0%
82 6
0.7%
81 3
 
0.3%
80 12
1.3%
79 10
1.1%

processor
Text

Distinct184
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:09.552430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length27.18589
Min length5

Characters and Unicode

Total characters24277
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)9.4%

Sample

1st row5th Gen AMD Ryzen 5 5600H
2nd row12th Gen Intel Core i3 1215U
3rd row11th Gen Intel Core i3 1115G4
4th row12th Gen Intel Core i5 1240P
5th rowApple M1
ValueCountFrequency (%)
gen 838
16.1%
intel 613
 
11.8%
core 583
 
11.2%
i5 287
 
5.5%
amd 265
 
5.1%
ryzen 257
 
4.9%
12th 214
 
4.1%
13th 207
 
4.0%
i7 148
 
2.8%
11th 132
 
2.5%
Other values (135) 1667
32.0%
2023-12-26T03:19:09.992566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4463
18.4%
e 2376
 
9.8%
n 1749
 
7.2%
1 1516
 
6.2%
t 1449
 
6.0%
5 1193
 
4.9%
G 933
 
3.8%
0 887
 
3.7%
h 832
 
3.4%
3 819
 
3.4%
Other values (41) 8060
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9566
39.4%
Decimal Number 6105
25.1%
Space Separator 4463
18.4%
Uppercase Letter 4141
17.1%
Format 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 933
22.5%
C 613
14.8%
I 609
14.7%
H 356
 
8.6%
U 327
 
7.9%
A 288
 
7.0%
M 281
 
6.8%
R 258
 
6.2%
D 257
 
6.2%
P 67
 
1.6%
Other values (10) 152
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
e 2376
24.8%
n 1749
18.3%
t 1449
15.1%
h 832
 
8.7%
l 673
 
7.0%
r 643
 
6.7%
o 629
 
6.6%
i 599
 
6.3%
z 257
 
2.7%
y 257
 
2.7%
Other values (9) 102
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 1516
24.8%
5 1193
19.5%
0 887
14.5%
3 819
13.4%
7 618
10.1%
2 550
 
9.0%
4 200
 
3.3%
6 151
 
2.5%
9 92
 
1.5%
8 79
 
1.3%
Space Separator
ValueCountFrequency (%)
4463
100.0%
Format
ValueCountFrequency (%)
‎ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13707
56.5%
Common 10570
43.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2376
17.3%
n 1749
12.8%
t 1449
10.6%
G 933
 
6.8%
h 832
 
6.1%
l 673
 
4.9%
r 643
 
4.7%
o 629
 
4.6%
C 613
 
4.5%
I 609
 
4.4%
Other values (29) 3201
23.4%
Common
ValueCountFrequency (%)
4463
42.2%
1 1516
 
14.3%
5 1193
 
11.3%
0 887
 
8.4%
3 819
 
7.7%
7 618
 
5.8%
2 550
 
5.2%
4 200
 
1.9%
6 151
 
1.4%
9 92
 
0.9%
Other values (2) 81
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24275
> 99.9%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4463
18.4%
e 2376
 
9.8%
n 1749
 
7.2%
1 1516
 
6.2%
t 1449
 
6.0%
5 1193
 
4.9%
G 933
 
3.8%
0 887
 
3.7%
h 832
 
3.4%
3 819
 
3.4%
Other values (40) 8058
33.2%
Punctuation
ValueCountFrequency (%)
‎ 2
100.0%

CPU
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Quad Core, 8 Threads
130 
Hexa Core, 12 Threads
126 
10 Cores (2P + 8E), 12 Threads
125 
Octa Core, 16 Threads
102 
12 Cores (4P + 8E), 16 Threads
83 
Other values (24)
327 

Length

Max length31
Median length30
Mean length24.673012
Min length8

Characters and Unicode

Total characters22033
Distinct characters33
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowHexa Core, 12 Threads
2nd rowHexa Core (2P + 4E), 8 Threads
3rd rowDual Core, 4 Threads
4th row12 Cores (4P + 8E), 16 Threads
5th rowOcta Core (4P + 4E)

Common Values

ValueCountFrequency (%)
Quad Core, 8 Threads 130
14.6%
Hexa Core, 12 Threads 126
14.1%
10 Cores (2P + 8E), 12 Threads 125
14.0%
Octa Core, 16 Threads 102
11.4%
12 Cores (4P + 8E), 16 Threads 83
9.3%
Dual Core, 4 Threads 55
6.2%
14 Cores (6P + 8E), 20 Threads 50
 
5.6%
Hexa Core (2P + 4E), 8 Threads 44
 
4.9%
Octa Core (4P + 4E), 12 Threads 43
 
4.8%
Dual Core, 2 Threads 36
 
4.0%
Other values (19) 99
11.1%

Length

2023-12-26T03:19:10.238780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
threads 867
18.1%
core 555
11.6%
421
 
8.8%
12 382
 
8.0%
cores 337
 
7.0%
8e 277
 
5.8%
16 222
 
4.6%
8 178
 
3.7%
hexa 170
 
3.6%
2p 169
 
3.5%
Other values (19) 1203
25.2%

Most occurring characters

ValueCountFrequency (%)
3888
17.6%
e 1929
 
8.8%
r 1759
 
8.0%
a 1422
 
6.5%
s 1204
 
5.5%
d 1003
 
4.6%
C 892
 
4.0%
o 892
 
4.0%
T 867
 
3.9%
h 867
 
3.9%
Other values (23) 7310
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9880
44.8%
Space Separator 3888
 
17.6%
Uppercase Letter 3156
 
14.3%
Decimal Number 2980
 
13.5%
Other Punctuation 866
 
3.9%
Close Punctuation 421
 
1.9%
Math Symbol 421
 
1.9%
Open Punctuation 421
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1929
19.5%
r 1759
17.8%
a 1422
14.4%
s 1204
12.2%
d 1003
10.2%
o 892
9.0%
h 867
8.8%
u 227
 
2.3%
x 170
 
1.7%
c 158
 
1.6%
Other values (2) 249
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
C 892
28.3%
T 867
27.5%
E 421
13.3%
P 421
13.3%
H 170
 
5.4%
O 158
 
5.0%
Q 136
 
4.3%
D 91
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 845
28.4%
2 685
23.0%
8 484
16.2%
4 402
13.5%
6 331
 
11.1%
0 209
 
7.0%
3 17
 
0.6%
5 7
 
0.2%
Space Separator
ValueCountFrequency (%)
3888
100.0%
Other Punctuation
ValueCountFrequency (%)
, 866
100.0%
Close Punctuation
ValueCountFrequency (%)
) 421
100.0%
Math Symbol
ValueCountFrequency (%)
+ 421
100.0%
Open Punctuation
ValueCountFrequency (%)
( 421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13036
59.2%
Common 8997
40.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1929
14.8%
r 1759
13.5%
a 1422
10.9%
s 1204
9.2%
d 1003
7.7%
C 892
6.8%
o 892
6.8%
T 867
6.7%
h 867
6.7%
E 421
 
3.2%
Other values (10) 1780
13.7%
Common
ValueCountFrequency (%)
3888
43.2%
, 866
 
9.6%
1 845
 
9.4%
2 685
 
7.6%
8 484
 
5.4%
) 421
 
4.7%
+ 421
 
4.7%
( 421
 
4.7%
4 402
 
4.5%
6 331
 
3.7%
Other values (3) 233
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3888
17.6%
e 1929
 
8.8%
r 1759
 
8.0%
a 1422
 
6.5%
s 1204
 
5.5%
d 1003
 
4.6%
C 892
 
4.0%
o 892
 
4.0%
T 867
 
3.9%
h 867
 
3.9%
Other values (23) 7310
33.2%

Ram
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
16GB
456 
8GB
369 
32GB
 
40
4GB
 
22
64GB
 
3
Other values (2)
 
3

Length

Max length4
Median length4
Mean length3.5610302
Min length3

Characters and Unicode

Total characters3180
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row8GB
2nd row8GB
3rd row8GB
4th row16GB
5th row8GB

Common Values

ValueCountFrequency (%)
16GB 456
51.1%
8GB 369
41.3%
32GB 40
 
4.5%
4GB 22
 
2.5%
64GB 3
 
0.3%
12GB 2
 
0.2%
2GB 1
 
0.1%

Length

2023-12-26T03:19:10.408693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T03:19:10.644091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
16gb 456
51.1%
8gb 369
41.3%
32gb 40
 
4.5%
4gb 22
 
2.5%
64gb 3
 
0.3%
12gb 2
 
0.2%
2gb 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
G 893
28.1%
B 893
28.1%
6 459
14.4%
1 458
14.4%
8 369
11.6%
2 43
 
1.4%
3 40
 
1.3%
4 25
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1786
56.2%
Decimal Number 1394
43.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 459
32.9%
1 458
32.9%
8 369
26.5%
2 43
 
3.1%
3 40
 
2.9%
4 25
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
G 893
50.0%
B 893
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1786
56.2%
Common 1394
43.8%

Most frequent character per script

Common
ValueCountFrequency (%)
6 459
32.9%
1 458
32.9%
8 369
26.5%
2 43
 
3.1%
3 40
 
2.9%
4 25
 
1.8%
Latin
ValueCountFrequency (%)
G 893
50.0%
B 893
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 893
28.1%
B 893
28.1%
6 459
14.4%
1 458
14.4%
8 369
11.6%
2 43
 
1.4%
3 40
 
1.3%
4 25
 
0.8%

Ram_type
Categorical

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
DDR4
499 
DDR5
166 
LPDDR5
145 
LPDDR4X
 
41
LPDDR4
 
14
Other values (7)
 
28

Length

Max length7
Median length4
Mean length4.5711086
Min length3

Characters and Unicode

Total characters4082
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowDDR4
2nd rowDDR4
3rd rowDDR4
4th rowLPDDR5
5th rowDDR4

Common Values

ValueCountFrequency (%)
DDR4 499
55.9%
DDR5 166
 
18.6%
LPDDR5 145
 
16.2%
LPDDR4X 41
 
4.6%
LPDDR4 14
 
1.6%
LPDDR4x 13
 
1.5%
Unified 7
 
0.8%
DDR3 3
 
0.3%
LPDDR5X 2
 
0.2%
DDR4- 1
 
0.1%
Other values (2) 2
 
0.2%

Length

2023-12-26T03:19:10.855537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ddr4 500
56.0%
ddr5 166
 
18.6%
lpddr5 145
 
16.2%
lpddr4x 54
 
6.0%
lpddr4 14
 
1.6%
unified 7
 
0.8%
ddr3 3
 
0.3%
lpddr5x 3
 
0.3%
ddr 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 1772
43.4%
R 886
21.7%
4 568
 
13.9%
5 314
 
7.7%
L 216
 
5.3%
P 216
 
5.3%
X 43
 
1.1%
x 14
 
0.3%
i 14
 
0.3%
U 7
 
0.2%
Other values (6) 32
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3140
76.9%
Decimal Number 885
 
21.7%
Lowercase Letter 56
 
1.4%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1772
56.4%
R 886
28.2%
L 216
 
6.9%
P 216
 
6.9%
X 43
 
1.4%
U 7
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
x 14
25.0%
i 14
25.0%
n 7
12.5%
f 7
12.5%
e 7
12.5%
d 7
12.5%
Decimal Number
ValueCountFrequency (%)
4 568
64.2%
5 314
35.5%
3 3
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3196
78.3%
Common 886
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1772
55.4%
R 886
27.7%
L 216
 
6.8%
P 216
 
6.8%
X 43
 
1.3%
x 14
 
0.4%
i 14
 
0.4%
U 7
 
0.2%
n 7
 
0.2%
f 7
 
0.2%
Other values (2) 14
 
0.4%
Common
ValueCountFrequency (%)
4 568
64.1%
5 314
35.4%
3 3
 
0.3%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1772
43.4%
R 886
21.7%
4 568
 
13.9%
5 314
 
7.7%
L 216
 
5.3%
P 216
 
5.3%
X 43
 
1.1%
x 14
 
0.3%
i 14
 
0.3%
U 7
 
0.2%
Other values (6) 32
 
0.8%

ROM
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
512GB
634 
1TB
188 
256GB
 
42
128GB
 
12
2TB
 
10
Other values (2)
 
7

Length

Max length5
Median length5
Mean length4.5487122
Min length3

Characters and Unicode

Total characters4062
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row512GB
2nd row512GB
3rd row512GB
4th row512GB
5th row256GB

Common Values

ValueCountFrequency (%)
512GB 634
71.0%
1TB 188
 
21.1%
256GB 42
 
4.7%
128GB 12
 
1.3%
2TB 10
 
1.1%
64GB 5
 
0.6%
32GB 2
 
0.2%

Length

2023-12-26T03:19:11.072539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T03:19:11.224529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
512gb 634
71.0%
1tb 188
 
21.1%
256gb 42
 
4.7%
128gb 12
 
1.3%
2tb 10
 
1.1%
64gb 5
 
0.6%
32gb 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B 893
22.0%
1 834
20.5%
2 700
17.2%
G 695
17.1%
5 676
16.6%
T 198
 
4.9%
6 47
 
1.2%
8 12
 
0.3%
4 5
 
0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2276
56.0%
Uppercase Letter 1786
44.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 834
36.6%
2 700
30.8%
5 676
29.7%
6 47
 
2.1%
8 12
 
0.5%
4 5
 
0.2%
3 2
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 893
50.0%
G 695
38.9%
T 198
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2276
56.0%
Latin 1786
44.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 834
36.6%
2 700
30.8%
5 676
29.7%
6 47
 
2.1%
8 12
 
0.5%
4 5
 
0.2%
3 2
 
0.1%
Latin
ValueCountFrequency (%)
B 893
50.0%
G 695
38.9%
T 198
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 893
22.0%
1 834
20.5%
2 700
17.2%
G 695
17.1%
5 676
16.6%
T 198
 
4.9%
6 47
 
1.2%
8 12
 
0.3%
4 5
 
0.1%
3 2
 
< 0.1%

ROM_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
SSD
872 
Hard-Disk
 
21

Length

Max length9
Median length3
Mean length3.1410974
Min length3

Characters and Unicode

Total characters2805
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSD
2nd rowSSD
3rd rowSSD
4th rowSSD
5th rowSSD

Common Values

ValueCountFrequency (%)
SSD 872
97.6%
Hard-Disk 21
 
2.4%

Length

2023-12-26T03:19:11.403447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T03:19:11.552545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ssd 872
97.6%
hard-disk 21
 
2.4%

Most occurring characters

ValueCountFrequency (%)
S 1744
62.2%
D 893
31.8%
H 21
 
0.7%
a 21
 
0.7%
r 21
 
0.7%
d 21
 
0.7%
- 21
 
0.7%
i 21
 
0.7%
s 21
 
0.7%
k 21
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2658
94.8%
Lowercase Letter 126
 
4.5%
Dash Punctuation 21
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 21
16.7%
r 21
16.7%
d 21
16.7%
i 21
16.7%
s 21
16.7%
k 21
16.7%
Uppercase Letter
ValueCountFrequency (%)
S 1744
65.6%
D 893
33.6%
H 21
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2784
99.3%
Common 21
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1744
62.6%
D 893
32.1%
H 21
 
0.8%
a 21
 
0.8%
r 21
 
0.8%
d 21
 
0.8%
i 21
 
0.8%
s 21
 
0.8%
k 21
 
0.8%
Common
ValueCountFrequency (%)
- 21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1744
62.2%
D 893
31.8%
H 21
 
0.7%
a 21
 
0.7%
r 21
 
0.7%
d 21
 
0.7%
- 21
 
0.7%
i 21
 
0.7%
s 21
 
0.7%
k 21
 
0.7%

GPU
Text

Distinct134
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:11.726492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length35
Mean length22.62374
Min length8

Characters and Unicode

Total characters20203
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)6.7%

Sample

1st row4GB AMD Radeon RX 6500M
2nd rowIntel UHD Graphics
3rd rowIntel Iris Xe Graphics
4th rowIntel Integrated Iris Xe
5th rowApple M1 Integrated Graphics
ValueCountFrequency (%)
intel 410
11.5%
graphics 324
 
9.1%
nvidia 305
 
8.6%
geforce 297
 
8.4%
rtx 267
 
7.5%
amd 229
 
6.4%
iris 207
 
5.8%
xe 199
 
5.6%
integrated 196
 
5.5%
radeon 176
 
5.0%
Other values (80) 941
26.5%
2023-12-26T03:19:12.055518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2684
 
13.3%
e 1787
 
8.8%
I 1407
 
7.0%
r 1039
 
5.1%
G 983
 
4.9%
t 803
 
4.0%
n 783
 
3.9%
a 722
 
3.6%
D 697
 
3.4%
c 630
 
3.1%
Other values (41) 8668
42.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9049
44.8%
Uppercase Letter 6781
33.6%
Space Separator 2684
 
13.3%
Decimal Number 1678
 
8.3%
Dash Punctuation 10
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 1407
20.7%
G 983
14.5%
D 697
10.3%
A 536
 
7.9%
X 521
 
7.7%
R 462
 
6.8%
B 318
 
4.7%
T 314
 
4.6%
V 306
 
4.5%
N 305
 
4.5%
Other values (10) 932
13.7%
Lowercase Letter
ValueCountFrequency (%)
e 1787
19.7%
r 1039
11.5%
t 803
8.9%
n 783
8.7%
a 722
8.0%
c 630
 
7.0%
i 570
 
6.3%
s 536
 
5.9%
o 485
 
5.4%
l 417
 
4.6%
Other values (8) 1277
14.1%
Decimal Number
ValueCountFrequency (%)
0 626
37.3%
4 278
16.6%
5 230
 
13.7%
6 191
 
11.4%
3 105
 
6.3%
8 83
 
4.9%
2 69
 
4.1%
1 57
 
3.4%
7 30
 
1.8%
9 9
 
0.5%
Space Separator
ValueCountFrequency (%)
2684
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Symbol
ValueCountFrequency (%)
® 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15830
78.4%
Common 4373
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1787
 
11.3%
I 1407
 
8.9%
r 1039
 
6.6%
G 983
 
6.2%
t 803
 
5.1%
n 783
 
4.9%
a 722
 
4.6%
D 697
 
4.4%
c 630
 
4.0%
i 570
 
3.6%
Other values (28) 6409
40.5%
Common
ValueCountFrequency (%)
2684
61.4%
0 626
 
14.3%
4 278
 
6.4%
5 230
 
5.3%
6 191
 
4.4%
3 105
 
2.4%
8 83
 
1.9%
2 69
 
1.6%
1 57
 
1.3%
7 30
 
0.7%
Other values (3) 20
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20202
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2684
 
13.3%
e 1787
 
8.8%
I 1407
 
7.0%
r 1039
 
5.1%
G 983
 
4.9%
t 803
 
4.0%
n 783
 
3.9%
a 722
 
3.6%
D 697
 
3.5%
c 630
 
3.1%
Other values (40) 8667
42.9%
None
ValueCountFrequency (%)
® 1
100.0%

display_size
Real number (ℝ)

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.173751
Minimum11.6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:12.233629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile14
Q114
median15.6
Q315.6
95-th percentile16
Maximum18
Range6.4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.93909503
Coefficient of variation (CV)0.061889444
Kurtosis0.56612155
Mean15.173751
Median Absolute Deviation (MAD)0
Skewness-0.80858315
Sum13550.16
Variance0.88189948
MonotonicityNot monotonic
2023-12-26T03:19:12.394580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15.6 464
52.0%
14 214
24.0%
16 112
 
12.5%
13.3 28
 
3.1%
16.1 21
 
2.4%
17.3 14
 
1.6%
14.1 7
 
0.8%
11.6 7
 
0.8%
17 5
 
0.6%
13.4 4
 
0.4%
Other values (8) 17
 
1.9%
ValueCountFrequency (%)
11.6 7
 
0.8%
13.3 28
 
3.1%
13.4 4
 
0.4%
13.5 2
 
0.2%
13.6 2
 
0.2%
14 214
24.0%
14.1 7
 
0.8%
14.2 4
 
0.4%
15 3
 
0.3%
15.3 2
 
0.2%
ValueCountFrequency (%)
18 1
 
0.1%
17.3 14
 
1.6%
17 5
 
0.6%
16.2 2
 
0.2%
16.1 21
 
2.4%
16 112
 
12.5%
15.6 464
52.0%
15.56 1
 
0.1%
15.3 2
 
0.2%
15 3
 
0.3%

resolution_width
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2035.3931
Minimum1080
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:12.536496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile1366
Q11920
median1920
Q31920
95-th percentile2880
Maximum3840
Range2760
Interquartile range (IQR)0

Descriptive statistics

Standard deviation426.07601
Coefficient of variation (CV)0.20933353
Kurtosis4.9916138
Mean2035.3931
Median Absolute Deviation (MAD)0
Skewness1.8360547
Sum1817606
Variance181540.77
MonotonicityNot monotonic
2023-12-26T03:19:12.691454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1920 680
76.1%
2560 70
 
7.8%
1366 41
 
4.6%
2880 40
 
4.5%
3840 14
 
1.6%
3200 11
 
1.2%
1080 8
 
0.9%
1600 5
 
0.6%
3024 4
 
0.4%
3456 4
 
0.4%
Other values (8) 16
 
1.8%
ValueCountFrequency (%)
1080 8
 
0.9%
1200 4
 
0.4%
1280 2
 
0.2%
1366 41
 
4.6%
1440 1
 
0.1%
1600 5
 
0.6%
1920 680
76.1%
2160 3
 
0.3%
2240 2
 
0.2%
2256 1
 
0.1%
ValueCountFrequency (%)
3840 14
 
1.6%
3456 4
 
0.4%
3200 11
 
1.2%
3072 1
 
0.1%
3024 4
 
0.4%
2880 40
4.5%
2560 70
7.8%
2496 2
 
0.2%
2256 1
 
0.1%
2240 2
 
0.2%

resolution_height
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1218.3247
Minimum768
Maximum3456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2023-12-26T03:19:12.833453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile1080
Q11080
median1080
Q31200
95-th percentile1920
Maximum3456
Range2688
Interquartile range (IQR)120

Descriptive statistics

Standard deviation326.75688
Coefficient of variation (CV)0.26820179
Kurtosis5.7277028
Mean1218.3247
Median Absolute Deviation (MAD)0
Skewness2.1707962
Sum1087964
Variance106770.06
MonotonicityNot monotonic
2023-12-26T03:19:13.003607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1080 577
64.6%
1200 101
 
11.3%
1600 53
 
5.9%
768 41
 
4.6%
1800 34
 
3.8%
1440 15
 
1.7%
1920 13
 
1.5%
2400 11
 
1.2%
2000 9
 
1.0%
2560 6
 
0.7%
Other values (12) 33
 
3.7%
ValueCountFrequency (%)
768 41
 
4.6%
1024 2
 
0.2%
1080 577
64.6%
1200 101
 
11.3%
1280 1
 
0.1%
1400 2
 
0.2%
1440 15
 
1.7%
1504 1
 
0.1%
1600 53
 
5.9%
1620 6
 
0.7%
ValueCountFrequency (%)
3456 1
 
0.1%
2560 6
 
0.7%
2400 11
 
1.2%
2234 2
 
0.2%
2160 6
 
0.7%
2000 9
 
1.0%
1964 4
 
0.4%
1920 13
 
1.5%
1864 2
 
0.2%
1800 34
3.8%

OS
Categorical

IMBALANCE 

Distinct14
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Windows 11 OS
782 
Windows 10 OS
 
28
DOS OS
 
18
Windows 11 OS
 
15
Mac OS
 
12
Other values (9)
 
38

Length

Max length18
Median length13
Mean length12.714446
Min length6

Characters and Unicode

Total characters11354
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowWindows 11 OS
2nd rowWindows 11 OS
3rd rowWindows 11 OS
4th rowWindows 11 OS
5th rowMac OS

Common Values

ValueCountFrequency (%)
Windows 11 OS 782
87.6%
Windows 10 OS 28
 
3.1%
DOS OS 18
 
2.0%
Windows 11 OS 15
 
1.7%
Mac OS 12
 
1.3%
Windows 10 OS 10
 
1.1%
Chrome OS 10
 
1.1%
Windows OS 9
 
1.0%
Ubuntu OS 2
 
0.2%
DOS 3.0 OS 2
 
0.2%
Other values (4) 5
 
0.6%

Length

2023-12-26T03:19:13.196620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
os 893
34.0%
windows 844
32.1%
11 798
30.4%
10 38
 
1.4%
dos 20
 
0.8%
mac 16
 
0.6%
chrome 10
 
0.4%
ubuntu 2
 
0.1%
3.0 2
 
0.1%
10.15.3 2
 
0.1%
Other values (4) 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1761
15.5%
1 1638
14.4%
S 914
8.1%
O 913
8.0%
o 855
7.5%
n 848
7.5%
i 848
7.5%
d 846
7.5%
W 844
7.4%
s 844
7.4%
Other values (23) 1043
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5177
45.6%
Uppercase Letter 2722
24.0%
Space Separator 1761
 
15.5%
Decimal Number 1686
 
14.8%
Other Punctuation 6
 
0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 855
16.5%
n 848
16.4%
i 848
16.4%
d 846
16.3%
s 844
16.3%
w 844
16.3%
a 20
 
0.4%
c 16
 
0.3%
r 13
 
0.3%
e 11
 
0.2%
Other values (7) 32
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 914
33.6%
O 913
33.5%
W 844
31.0%
D 20
 
0.7%
M 16
 
0.6%
C 11
 
0.4%
U 2
 
0.1%
A 1
 
< 0.1%
H 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1638
97.2%
0 42
 
2.5%
3 4
 
0.2%
5 2
 
0.1%
Space Separator
ValueCountFrequency (%)
1761
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7899
69.6%
Common 3455
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 914
11.6%
O 913
11.6%
o 855
10.8%
n 848
10.7%
i 848
10.7%
d 846
10.7%
W 844
10.7%
s 844
10.7%
w 844
10.7%
D 20
 
0.3%
Other values (16) 123
 
1.6%
Common
ValueCountFrequency (%)
1761
51.0%
1 1638
47.4%
0 42
 
1.2%
. 6
 
0.2%
3 4
 
0.1%
5 2
 
0.1%
2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1761
15.5%
1 1638
14.4%
S 914
8.1%
O 913
8.0%
o 855
7.5%
n 848
7.5%
i 848
7.5%
d 846
7.5%
W 844
7.4%
s 844
7.4%
Other values (23) 1043
9.2%

warranty
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
819 
2
 
59
3
 
9
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters893
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Length

2023-12-26T03:19:13.362628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-26T03:19:13.481612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 893
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 893
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Interactions

2023-12-26T03:19:03.817400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:56.953607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:58.496962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:59.580965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:00.534734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:01.602262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:02.725356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:03.964289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:57.523167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:58.654369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:59.729605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:00.660703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:01.784363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:02.849252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:04.124197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:57.666462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:58.812337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:59.859663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:00.800003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:01.950386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:03.006445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:04.249198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:57.813212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:58.947236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:59.984761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:00.921003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:02.089845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:03.178365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:04.464677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:57.975441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:59.102858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:00.116119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:01.083013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:02.248851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:03.361067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:04.642084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:58.136904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:59.250504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:00.244733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:01.237657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:02.408197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:03.540852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:04.798699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:58.249950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:18:59.420754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:00.404736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:01.413737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:02.566079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-26T03:19:03.686669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-26T03:19:13.634264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CPUOSROMROM_typeRamRam_typeUnnamed: 0Unnamed: 0.1branddisplay_sizepriceresolution_heightresolution_widthspec_ratingwarranty
CPU1.0000.4210.4470.2600.5550.425-0.050-0.0500.251-0.023-0.278-0.156-0.107-0.0570.088
OS0.4211.0000.3840.3970.2460.331-0.043-0.0430.4310.1220.046-0.004-0.0100.0450.352
ROM0.4470.3841.0000.4400.4900.280-0.078-0.0780.429-0.149-0.332-0.285-0.288-0.2940.040
ROM_type0.2600.3970.4401.0000.3730.1810.0790.0790.2780.0390.1510.1430.1220.0150.000
Ram0.5550.2460.4900.3731.0000.317-0.015-0.0150.481-0.081-0.570-0.342-0.256-0.2030.061
Ram_type0.4250.3310.2800.1810.3171.0000.0760.0760.406-0.0410.3510.3790.2570.1880.092
Unnamed: 0-0.050-0.043-0.0780.079-0.0150.0761.0001.0000.1650.0050.1850.0820.0100.0760.105
Unnamed: 0.1-0.050-0.043-0.0780.079-0.0150.0761.0001.0000.1590.0050.1850.0820.0100.0760.109
brand0.2510.4310.4290.2780.4810.4060.1650.1591.0000.0150.020-0.003-0.0340.0060.455
display_size-0.0230.122-0.1490.039-0.081-0.0410.0050.0050.0151.0000.2680.1130.1750.3470.082
price-0.2780.046-0.3320.151-0.5700.3510.1850.1850.0200.2681.0000.6130.5150.4320.102
resolution_height-0.156-0.004-0.2850.143-0.3420.3790.0820.082-0.0030.1130.6131.0000.7020.2760.082
resolution_width-0.107-0.010-0.2880.122-0.2560.2570.0100.010-0.0340.1750.5150.7021.0000.2530.070
spec_rating-0.0570.045-0.2940.015-0.2030.1880.0760.0760.0060.3470.4320.2760.2531.0000.089
warranty0.0880.3520.0400.0000.0610.0920.1050.1090.4550.0820.1020.0820.0700.0891.000

Missing values

2023-12-26T03:19:05.031270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-26T03:19:05.393543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0.1Unnamed: 0brandnamepricespec_ratingprocessorCPURamRam_typeROMROM_typeGPUdisplay_sizeresolution_widthresolution_heightOSwarranty
000HPVictus 15-fb0157AX Gaming Laptop4990073.0000005th Gen AMD Ryzen 5 5600HHexa Core, 12 Threads8GBDDR4512GBSSD4GB AMD Radeon RX 6500M15.61920.01080.0Windows 11 OS1
111HP15s-fq5007TU Laptop3990060.00000012th Gen Intel Core i3 1215UHexa Core (2P + 4E), 8 Threads8GBDDR4512GBSSDIntel UHD Graphics15.61920.01080.0Windows 11 OS1
222AcerOne 14 Z8-415 Laptop2699069.32352911th Gen Intel Core i3 1115G4Dual Core, 4 Threads8GBDDR4512GBSSDIntel Iris Xe Graphics14.01920.01080.0Windows 11 OS1
333LenovoYoga Slim 6 14IAP8 82WU0095IN Laptop5972966.00000012th Gen Intel Core i5 1240P12 Cores (4P + 8E), 16 Threads16GBLPDDR5512GBSSDIntel Integrated Iris Xe14.02240.01400.0Windows 11 OS1
444AppleMacBook Air 2020 MGND3HN Laptop6999069.323529Apple M1Octa Core (4P + 4E)8GBDDR4256GBSSDApple M1 Integrated Graphics13.32560.01600.0Mac OS1
555AcerExtensa EX214-53 Laptop3999062.00000012th Gen Intel Core i5 1240P12 Cores (4P + 8E), 16 Threads8GBDDR4512GBSSDIntel Iris Xe Graphics14.01920.01080.0Windows 11 OS1
666DellInspiron 3520 D560896WIN9B Laptop3679060.00000012th Gen Intel Core i3 1215UHexa Core (2P + 4E), 8 Threads8GBDDR4512GBSSDIntel UHD Graphics15.61920.01080.0Windows 11 OS1
777AcerNitro V ANV15-51 2023 Gaming Laptop7699063.00000013th Gen Intel Core i5 13420HOcta Core (4P + 4E), 12 Threads16GBDDR5512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
888AsusVivobook 15 X1502ZA-EJ523WS Laptop4899064.00000012th Gen Intel Core i5 12500H12 Cores (4P + 8E), 16 Threads8GBDDR4512GBSSDIntel Iris Xe15.61920.01080.0Windows 11 OS1
999SamsungGalaxy Book2 Pro 13 Laptop7499068.00000012th Gen Intel Core i5 1240P12 Cores (4P + 8E), 16 Threads16GBLPDDR5512GBSSDIntel Iris Xe Graphics13.31080.01920.0Windows 11 OS1
Unnamed: 0.1Unnamed: 0brandnamepricespec_ratingprocessorCPURamRam_typeROMROM_typeGPUdisplay_sizeresolution_widthresolution_heightOSwarranty
8839211010DellG15-5530 Gaming Laptop11999073.00000013th Gen Intel Core i7 13650HX14 Cores (6P + 8E)16GBDDR5512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
8849221011Dell‎G16-7630 Gaming Laptop18749079.00000013th Gen Intel Core i9 13900HX24 Cores (8P + 16E), 32 Threads16GBDDR51TBSSD8GB NVIDIA GeForce RTX 406016.02560.01600.0Windows 11 OS1
8859231012DellG15-5530 2023 Gaming Laptop12569975.00000013th Gen Intel Core i7 13650HX14 Cores (6P + 8E)16GBDDR5512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
8869241013AcerAspire Vero AV14-52P NX.KJTSI.002 Laptop4999069.32352913th Gen Intel Core i3 1315UHexa Core (2P + 4E), 8 Threads16GBLPDDR4X512GBSSDIntel Integrated UHD14.01920.01080.0Windows OS1
8879251014AcerAspire 5 A515-58M NX.KHGSI.002 Gaming Laptop5699069.32352913th Gen Intel Core i5 1335U10 Cores (2P + 8E), 12 Threads16GBLPDDR5512GBSSDIntel Integrated Iris Xe15.61920.01080.0Windows 11 OS1
8889261015AsusVivobook 15X 2023 K3504VAB-NJ321WS Laptop4499069.32352913th Gen ‎Intel Core i3 1315UHexa Core (2P + 4E), 8 Threads8GBDDR4512GBSSDIntegrated Intel UHD Graphics15.61920.01080.0Windows 11 OS1
8899271016AsusTUF A15 FA577RM-HQ032WS Laptop11000071.0000006th Gen AMD Ryzen 7 6800HOcta Core, 16 Threads16GBDDR1TBSSD6GB NVIDIA GeForce RTX 306015.62560.01440.0Windows 11 OS1
8909281017AsusROG Zephyrus G14 2023 GA402XV-N2034WS Gaming Laptop18999089.0000007th Gen AMD Ryzen 9 7940HSOcta Core, 16 Threads32GBDDR51TBSSD8GB NVIDIA GeForce RTX 406014.02560.01600.0Windows 11 OS1
8919291018AsusTUF Gaming F15 2023 FX507VU-LP083WS Gaming Laptop12999073.00000013th Gen Intel Core i7 13700H14 Cores (6P + 8E), 20 Threads16GBDDR4512GBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1
8929301019AsusTUF Gaming A15 2023 FA577XU-LP041WS Gaming Laptop13199084.0000007th Gen AMD Ryzen 9 7940HSOcta Core, 16 Threads16GBDDR41TBSSD6GB NVIDIA GeForce RTX 405015.61920.01080.0Windows 11 OS1